(1) Field of the Invention
The invention is generally related to the field of signal processing, primarily digital signal processing, and more particularly to the field of systems for performing target motion analysis. The invention provides an improved adaptive statistical filtering system for enhancing noise discrimination.
(2) Description of the Prior Art
Relative motion analysis is employed in a number of applications, including target motion analysis and robotics systems, to determine the range, bearing, speed, velocity and direction of motion (velocity) of an object relative to a sensor. In relative motion analysis, either or both of the object and the sensor may be stationary or in motion. Typical relative motion analysis systems include a sensor, a motion analysis processing arrangement and a filter. The sensor, typically a sonar or radar sensor, provides a data stream representing signals emanating from or reflected off the object, as received or observed by the sensor. The data stream includes not only the desired signal, representing information as to the object's position and motion relative to the sensor, but also undesirable random noise, such as that induced by the medium through which the signal travels. The filter is provided to reduce or eliminate the noise, effectively extracting from the data stream the portion representative of the object's position and motion relative to the sensor. The filter provides the extracted portion to the motion analysis processing arrangement, which uses the data stream to generate estimates of the position, and velocity of the object relative to the sensor.
Prior relative motion analysis systems that made use of single fixed-order filters did not provide optimum performance of extracting the desired portion of the data stream. On the other hand, a system including a plurality of filters of diverse orders, and an arrangement for determining the filter whose order provides an optimal performance is disclosed in U.S. Pat. No. 5,144,595, issued Sep. 1, 1992, to Marcus L. Graham, et al., entitled Adaptive Statistical Filter Providing Improved Performance For Target Motion Analysis Discrimination, assigned to the assignee of the present application, which patent is hereby incorporated by reference. The system described in the Graham patent provides better performance than prior systems that used only single fixed-order filters. However, the design of the system described in the Graham patent is based on the assumption that the noise component is completely random and that the noise component of the data stream at each point in time is completely uncorrelated to the noise component at successive points in time. This assumption is not necessarily correct. For many known sensors and data gathering techniques there is often a correlation between the portion of the noise component in the data stream at successive points in time.
Typically, for example, the data stream representation at a particular point in time as provided by typical sensors used with a relative motion analysis system is not just the instantaneous value of the signal as detected by the sensor at that point in time. Indeed, the value D.sub.n that is provided for a particular point in time T.sub.n is essentially taken over a window, termed the sample integration time, that the sensor requires to actually determine a value. In that technique, the sensor will receive a continuous stream of data dT, and, for each time t.sub.T for which it provides a value D.sub.T, it will report the value as effectively the normalized sum of the value d.sub.T actually detected by the sensor at time t.sub.T, and weighted values detected at selected previous points in time in the window defining the sample integration time. Otherwise stated, for each time t.sub.T EQU D.sub.T =A[d.sub.T +.SIGMA.c.sub.k d.sub.T -k] [Eqn. 1]
where the sum is taken over "k" from "1" to "K". In equation Eqn. 1, each "Ck" represents a weighting coefficient, and "A" represents a normalization factor. Typically, the weighting coefficients c.sub.k will be positive, but will decrease toward zero as "k" increases, so that the contribution of components d.sub.T-k to the data value D.sub.T as provided by the sensor will decrease as their displacement from time t.sub.T increases. Otherwise stated, the values d.sub.D-k as detected by the sensor which are detected by the sensor closer to the time t.sub.T at which the sensor will provide a data value D.sub.T will provide a greater contribution to the value of D.sub.T.
The particular function selected for the weighting coefficients ck will be selected based on a number of factors; in the typical case, the weighting coefficients may be, for example, an exponential function, in which case the coefficients will decrease exponentially. In any case, it will be apparent from equation Eqn. 1, since that D.sub.T, the value of the data stream at each point in time as provided by the sensors, includes some components from values d.sub.T-K to d.sub.T as detected by the sensor, and since these values include a noise component as detected by the sensor at the respective points in time in the window, if, as is generally the case, there is an overlap in windows for successive points in time t.sub.T, there will be a correlation between the noise component of values D.sub.T provided at successive points in time.